Evidence map›Paper›PMID 27538399›Full record

ArticleMedical & biological engineering & computing2017

Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.

Jiansen Li, Ying Song, Zhen Zhu, Jun Zhao

Abstract read
PubMed Publisher
In one paragraph

Article in Medical & biological engineering & computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Orthogonal tensor dictionary learning for accelerated dynamic MRI.Medical & biological engineering & computing · 2019
    Article
  2. Article
  3. MR image reconstruction via guided filter.Medical & biological engineering & computing · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jiansen LiSchool of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd., Minhang, Shanghai, 200240, China.
Ying SongDepartment of Radiation Oncology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
Zhen ZhuDepartment of Radiology, Children's Hospital of Shanghai, Shanghai Jiao Tong University, Shanghai, China.
Jun ZhaoSchool of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd., Minhang, Shanghai, 200240, China. junzhao@sjtu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local details of training sets images. The prior knowledge helps to improve the reconstruction quality greatly. This method has been successfully applied in magnetic resonance (MR) image reconstruction. However, it relies heavily on the training sets, and dictionaries are fixed and nonadaptive. In this research, we improve Dual-DL by using self-adaptive dictionaries. The low- and high-resolution dictionaries are updated correspondingly along with the image updating stage to ensure their self-adaptivity. The updated dictionaries incorporate both the prior information of the training sets and the test image directly. Both dictionaries feature improved adaptability. Experimental results demonstrate that the proposed method can efficiently and significantly improve the quality and robustness of MR image reconstruction.

Indexed as

BrainHumansImage Processing, Computer-AssistedLearningMagnetic Resonance ImagingCompressed sensingDual-dictionary learningImage reconstructionMagnetic resonance imagingSelf-adaptive dictionary

Identifiers

PMID27538399

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.